quantitative research course

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College of Liberal Arts and Sciences

Department of Psychological Sciences

Graduate certificate in quantitative research methods.

UConn’s Graduate Certificate in Quantitative Research Methods is designed to give students the tools they need to study complex, real-world problems in social science.

Program participants learn about statistical and computational techniques, with courses that span research design, measurement, modeling, data analysis, and interpretation. This 12-credit certificate is open to students enrolled in any UConn graduate program.

Read Our Admissions Requirements

Program Overview

The UConn Graduate Certificate in Quantitative Research Methods provides students with a thorough background in quantitative research methods in the psychological sciences that extends the basic research methods training required by their respective graduate programs. The program draws students from many backgrounds, departments, and schools.

Requirements and Courses

Students are required to complete two PSYC prerequisite courses plus an additional 12 credits of graduate coursework in quantitative methods selected from a list of courses below. Students work with their major advisor to build a custom Plan of Study that meets their career goals and research interests. Students who successfully complete the program requirements and submit a completed and approved Plan of Study to Degree Audit in the Office of the Registrar will receive a Certificate in Quantitative Research Methods from the UConn Graduate School.

Prerequisite Courses

Prerequisites for participation in the program are completion of PSYC 5104 and 5105. Foundations of Research in the Psychological Sciences I and II, or equivalent courses. Students who wish to substitute equivalent coursework for these two courses with other graduate-level introductory statistics courses need to submit a petition to the Program Director. The petition should include a syllabus of the equivalent course and other documentation if available (e.g., exam examples) to support the petition and facilitate the decision-making by the Program Committee.

Qualifying Courses

Participants in the program complete 12 credits of graduate coursework in quantitative methods selected from the following list of courses. Students who wish to petition for a course that is not on this list may do so by contacting the Program Director with documentation of the course content (e.g., syllabus, exam examples). Such courses cannot be from other institutions. Additionally, the Graduate School requires certificate students to earn at least a B (3.00) in all coursework.

AH 6005. Multilevel Mediation-Moderation Modeling for Health Sciences

AH 6015. Use of Large Population-Based Datasets for Health Promotion

PSYC 5131. Meta-analysis: Theory and Practice

PSYC 5332. Research Design and Test Construction

PSYC 5554. Advanced Nonlinear Dynamics

PSYC 5670. Current Topics in I/O Psych (Multivariate Approaches to Survey Data)

PSYC 5701. Experimental Social Psychology

PSYC 5702. Field Research Methods

PSYC 6130. Measurement and Scaling

PSYC 6783. Tools to Analyze Language

EPSY 6611. Hierarchical Linear Modeling

EPSY 6615. Structural Equation Modeling

EPSY 6651. Introduction to Methods for Causal Inference Using Educational Data

EPSY 6637. Item Response Theory

HDFS 5002. Special Topics (Longitudinal Methods, Meta Analysis, others by Program Director approval)

MGMT 6206. Applied Research in Management (Seminar in Multilevel Theories, Methods, & Analyses in Management)

PUBH 5434. Topics in Intermediate Biostatistics

PUBH 5435. Statistical Methods in Epidemiology

PUBH 5468. Occupational and Environmental Epidemiology

SOCI 5203. Quantitative Research I

STAT 3375Q. Introduction to Mathematical Statistics

STAT 5825. Applied Time Series

Note: Additional courses are acceptable with prior approval. If you are unsure about whether a course qualifies you can contact the Program Director. Please check the graduate catalog, department websites, and instructors for course availability. Not all courses are available each academic year. In some divisions courses on different topics can share the same course number (e.g., “Current Topics in…”) and can, thus, qualify twice.

Plan of Study and Completion of the Program

Students interested in the program will design an individualized plan of study, in consultation with their major advisor. The plan will outline a path to best serve the student’s research needs, interests, and career goals. Students may also wish to consult with one or more members of the Program’s Executive Committee about the courses they plan to take and their expected date of completion.

Download the Plan of Study for the Certificate Program.

The final Plan of Study form should be signed by the Program Director and then submitted to Degree Audit in the Office of the Registrar by emailing it to [email protected] . Once the paperwork is processed, the Graduate School will issue the certificate.

Please bear in mind that students cannot apply for certificate completion after graduating from their master’s or Ph.D. program unless they want to continue paying student fees. Thus, it is important to complete the paperwork in the semester you are graduating. In addition, the Office of the Registrar issues deadlines each semester for submitting Plans of Study .

The certificate program is administered by faculty in the Departments of Psychological Sciences, Sociology, and Educational Psychology.

Executive Committee

James Dixon

James Green

Vicki Magley

Stephanie Milan

Ian Stevenson , Program Director

Program Faculty

James Dixon (PAC, Psychological Sciences)

James Green (Developmental, Psychological Sciences)

Blair Johnson (Social, Psychological Sciences)

Vicki Magley (I/O, Psychological Sciences)

Betsy McCoach (Educational Psychological Sciences)

Tania Huedo Medina (Allied Health)

Stephanie Milan (Clinical, Psychological Sciences)

Felicia Pratto (Social, Psychological Sciences)

Ian Stevenson (Behavioral Neuroscience, Psychological Sciences)

David Weakliem (Sociology)

Students must apply to the Graduate Certificate in Quantitative Research Methods via The Graduate School’s online application system. The application process is managed by The Graduate School, not the Department of Psychological Sciences, and is similar to the master’s/Ph.D. program application process.

Apply to the Graduate School

Application Requirements and Costs

Applicants must be currently enrolled in a UConn graduate program to apply, and they must submit an online application via The Graduate School’s website. The application fee is automatically waived for students currently enrolled in a UConn graduate program (certificate, master’s, or doctoral) when they apply to another graduate program.

In addition, applicants are required to upload unofficial copies of their transcripts to the online application system.

For questions about the Graduate Certificate in Quantitative Research Methods, please contact the program director:

Ian Stevenson

Associate Professor of Psychological Sciences [email protected]

Students interested in the certificate should contact the Psychological Sciences Graduate Program Coordinator at [email protected] to coordinate the appropriate forms and records for admission.

Quantitative and Qualitative Research

• I NEED TO . . .

What is Quantitative Research?

• What is Qualitative Research?
• Quantitative vs Qualitative
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• Step 6: Combining All Searches
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• What Kind of Article is This?
• More Research Help This link opens in a new window

Quantitative methodology is the dominant research framework in the social sciences. It refers to a set of strategies, techniques and assumptions used to study psychological, social and economic processes through the exploration of numeric patterns . Quantitative research gathers a range of numeric data. Some of the numeric data is intrinsically quantitative (e.g. personal income), while in other cases the numeric structure is  imposed (e.g. ‘On a scale from 1 to 10, how depressed did you feel last week?’). The collection of quantitative information allows researchers to conduct simple to extremely sophisticated statistical analyses that aggregate the data (e.g. averages, percentages), show relationships among the data (e.g. ‘Students with lower grade point averages tend to score lower on a depression scale’) or compare across aggregated data (e.g. the USA has a higher gross domestic product than Spain). Quantitative research includes methodologies such as questionnaires, structured observations or experiments and stands in contrast to qualitative research. Qualitative research involves the collection and analysis of narratives and/or open-ended observations through methodologies such as interviews, focus groups or ethnographies.

Coghlan, D., Brydon-Miller, M. (2014).  The SAGE encyclopedia of action research  (Vols. 1-2). London, : SAGE Publications Ltd doi: 10.4135/9781446294406

What is the purpose of quantitative research?

The purpose of quantitative research is to generate knowledge and create understanding about the social world. Quantitative research is used by social scientists, including communication researchers, to observe phenomena or occurrences affecting individuals. Social scientists are concerned with the study of people. Quantitative research is a way to learn about a particular group of people, known as a sample population. Using scientific inquiry, quantitative research relies on data that are observed or measured to examine questions about the sample population.

Allen, M. (2017).  The SAGE encyclopedia of communication research methods  (Vols. 1-4). Thousand Oaks, CA: SAGE Publications, Inc doi: 10.4135/9781483381411

How do I know if the study is a quantitative design?  What type of quantitative study is it?

Quantitative Research Designs: Descriptive non-experimental, Quasi-experimental or Experimental?

Studies do not always explicitly state what kind of research design is being used.  You will need to know how to decipher which design type is used.  The following video will help you determine the quantitative design type.

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• Quantitative Research Design and Analysis in Education and Policy Research

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Provided by: Cambridge Research Methods (CaRM)

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Quantitative Research Design and Analysis in Education and Policy Research New

The module provides a practical guide to designing and developing a research project based on quantitative dates. It focuses on key aspects of research design, how to work with theory, identify key concepts and operationalise them with quantitative data. It will explore the use of applied statistical methods for data analysis, their applications in academic research, and how to interpret statistical outputs. Although the illustrative examples are mainly drawn from education and policy research, the statistical and design knowledge and skills acquired via this module are also applicable to other social sciences research topics and areas.

The module consists of four lectures (two-hours per session) including:

• Lecture 1: Introduction to quantitative research design
• Lecture 2: Key statistical concepts and methods
• Lecture 3: Applied social statistics in education research
• Lecture 4: Education and social policy evaluation

Lecture 1 will focus on how to design quantitative studies, including formulating research questions, engaging with theoretical and empirical evidence, developing hypothesises, as well as preparing relevant data. Lecture 2 will cover some of the widely used statistical toolkits for data description and hypothesis testing, such as graphs, z-score, conference intervals, parametric and non-parametric tests, correlation and regression analyses. Lecture 3 applies the principles of research design and key statistical methods to examples drawn from education research. It will highlight regression analyses and the interpretation of statistical outputs. Lecture 4 will introduce a few causal inference methods, such as matching, instrumental variables, difference-in-differences, and regression discontinuity design, which are commonly used in social policy evaluations.

• Postgraduate students and staff
• Further details regarding eligibility criteria are available here

By the end of the module, students will be able to:

• Understand how to design quantitative studies
• Understand key statistical theories and methods widely used in academic research
• Analyse and interpret quantitative research papers and reports
• Understand causal inference methods widely used in policy evaluations

Teaching will take place in person, with each session lasting for 2 hours. No specific lab sessions are planned; however, Stata codes will be provided whenever relevant to facilitate learning and practice.

The module uses Stata software for practical examples. Basic knowledge of Stata is recommended to maximise the learning experience.

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Quantitative Research

University of California, Davis via Coursera Help

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• Getting Started and Preparing to Design a Quantitative Survey for Market Research
• In this module, you will be able to identify the business objectives and assess the suitability for quantitative research. You will be able to build on qualitative findings to help inform a survey. You will be able to discuss the various types of surveys and take steps to design and implement a survey. You will also be able to recognize and avoid common survey pitfalls.
• Designing and Implementing a Quantitative Survey
• In this module, you will be able to delineate the market segment and select your survey method. You will be able to identify the appropriate sample sources and determine appropriate sample size while maximizing your response rate. You will be able to design a questionnaire and appropriate response options. You will be able to perform quality control on survey questions and test and launch your survey.
• Analyzing a Survey
• In this module, you will be able to review your survey data and look for any errors. You will be able to analyze your survey conducting descriptive and inferential analysis techniques to test your hypotheses. You will be able to conduct association analysis and causal analysis with your data. Please note that this module features a fair amount of advanced statistics related math. Throughout the module, optional activities setup as practice quizzes have been provided to help reinforce what you're learning.
• Assess Approaches and Interpret Quantitative Study Results
• In this module, you will be able to assess the various approaches for analyzing your data. You will be able to consider multiple variables throughout your analysis and be able to interpret your results. Like the last module, this module features a fair amount of advanced statistics related math. Several optional activities setup as practice quizzes have been provided to help reinforce what you're learning.

Susan Berman and Olivier Rubel, PhD

• market research
• quantitative research
• united states

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RES/710 Statistical Research Methods and Design I

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Course level:  Doctorate Courses

Doctoral practitioners are resolute in their commitment to identifying, informing, and affecting both individual and organizational change and innovation. Generating actionable interventions requires an extensive investigation of situational factors and a foundational understanding of research methodology. In this course, students explore the foundations of statistics used in quantitative research by actively engaging in processes focused on evaluation, appraisal, and application.

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Advanced methods in nursing research.

Building upon the foundational knowledge acquired in the quantitative and qualitative research method courses, this course will introduce advanced research methods in nursing research. It explores three pivotal areas in nursing research: Health Services Research, Omics Research, and Data Science Research. Students will learn theoretical principles or frameworks for the three advanced methods, and critically review the commonly used research methods. Additionally, the course examines the interplay between science, policy, and healthcare delivery and identifies critical questions shaping the future policy research agenda.

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Professional/Short course Quantitative Health Research

15 credit level 7 module

Page last updated 24 September 2024

Introduction

This module is relevant for the MSc Public Health and MSc Environmental Health students.

Accreditations and partnerships:

The 15 credit Quantitative Health Research module provides an introduction and critical examination of quantitative approaches to health research, with particular focus on epidemiology and population-based public health research.

This level 7 (Masters level) module explores the key features of epidemiological investigation, examines key terminology, ideas and concepts concerning quantitative methodology, and teaches quantitative methods and skills for undertaking primary research and for interpreting published epidemiological research.

Additionally, the module examines how epidemiological knowledge and skills are useful for a wide spectrum of professionals whose work impacts population health.

After successful completion of this module, you will be able to:

• Understand the historical basis for epidemiology in public health.
• Critically analyse the strengths, limitations and issues to consider with different types of epidemiological studies.
• Assess the sources of evidence routinely used as a basis for health policy and practice, including strengths and limitations.
• Interpret and apply the results of basic statistical analyses, particularly inferential statistics.
• Use appropriate tools to critically appraise the evidence used in public health.
• Understand how evidence is used in the policy process.

Careers / Further study

This module contributes towards the MSc Environmental Health and the MSc Public Health.

The module syllabus for this level 7 (Masters level) typically includes:

• History of epidemiology and our understanding of disease causation in populations
• Characteristics, strengths and weaknesses of epidemiological studies
• Basic statistics descriptive and inferential
• Introduction to statistical tests, including parametric and non-parametric
• Controlling for bias and confounding in epidemiological studies
• The concept of evidence-based policy and practice
• Hierarchies of evidence
• Tools and techniques in critical appraisal
• The dissemination and communication of evidence
• Ethical issues in the use of data

Learning and Teaching

Scheduled learning includes; lectures, seminars, groupwork and workshops. Independent learning includes; hours engaged with essential reading, workshop preparation, assignment preparation and completion and self-directed study. Technology Enhanced Learning will supplement taught sessions, to enable all students to access essential and supplementary learning materials via Blackboard. Module support will be supplemented with an electronic discussion board (either via Blackboard or via a dedicated blog).Podcasts/video will be used to accompany lecture input.

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